Course Content

  • Python (Cambridge Spark focuses on the use of Python)1
  • Data privacy, ethics and regulations
  • Data visualisations (mainly done in Python)
  • Power BI & Tableau (optional)
  • Databases & SQL2

Course Content (continued)

  • Why data science is good for businesses
  • Maths for data science
  • Time series analysis
  • Descriptive, Predictive and Prescriptive analytics
    • Inferential analysis
  • Introduction to machine learning
    • Predictive analysis e.g. forecasting and categorization
  • Text-mining, JSON and APIs

Knowledge, Skills and Behaviours (KSBs)

  • Two separate sets: one set has to be demonstrated across your portfolio, one has to be demonstrated during your EPA project
  • Knowledge: current legislation, data types, principles of statistics & different types of analytics
  • Skills: apply statistical methodologies, convert data into visualisations, assess the impact of user experience
  • Behaviours: demonstrate initiative and resilience, work independently and collaboratively

The Data Analysis Life Cycle

Timeline & Commitment

  • Approximately 16 months from the introductory webinar to the End Point Assessment
  • Typically 3 projects that form a portfolio of work + 1 End Point Assessment project
  • “Off The Job” hours: minimum 6 hours per week.
  • The End Point Assessment
    • A professional discussion focusing on your portfolio
    • A presentation on your End Point Assessment project
    • Questions about your End Point Assessment project

Projects that I undertook

  • Automation of data processing for the Non-Bedded Community Modelling Tool
  • Improving visualisations for the HIOW Mental Health S136 report
  • Exploration of forecasting methods applied to cancer waiting times data
  • Using regression to predict improvement in Mental Health PROMs scores
  • Investigating the relationship between health inequalities factors on Adult Social Care waiting times, and those waiting times on Emergency Department attendances

Project outputs

Community data processing automation

Improving S136 visualisations

Exploring forecasting methods

Regression output

Adult Social Care deep-dive. Custom chart created in Python

What I have done since

  • Contributed to the running of Code Club
  • Developed a Python script that searches Outlook and downloads attachments
  • Supported testing Fabric as a platform for hosting data science projects by creating test machine learning workflows
  • Wrote Python webscraping and data concatenation scripts to speed up data processing on a number of projects.
  • Overlaid Fingertips data onto maps to support preliminary investigations for a Transformation & Consultancy project.

Reflections

  • Keep the Data Analysis Life Cycle in mind for all your projects
  • Have the list of KSBs open when you are scoping and writing up your projects
  • Stay focused on the STAR method when writing up your portfolio projects
    • Situation
    • Task
    • Action
    • Result

Questions?

Quarto

An open-source scientific and technical publishing system

https://quarto.org/